Since roads are influenced by a plenty of factors such as natural environment, traffic load, material properties, etc. in the process of operation, various damages will appear gradually on their surfaces. Cracks, as one of the most primary damage types of asphalt pavement, seriously affect the driving speed and driving safety, aggravate the vehicle wear, and also shorten the service life of the asphalt pavement. In order to save the maintenance resources, and guarantee the driving safety and comfort, there is a need for rapidly and accurately obtaining parameter information such as the location, area and degree etc. of the road damages, so as to provide the basis for the traffic management departments to objectively evaluate the road quality and scientifically determine the maintenance management schemes.
At present, with the development of technologies such as sensors, automatic control and computer etc., the automatic collection apparatuses for pavement images have approached to be mature, while the later crack identification is still in a man-machine combination or even completely manual way, which has heavy workload and low efficiency. However, in most cases, the number of images with damages in the collected highway images is often less than 10% of the total number of images. If an effective classification method for images with and without damages can be provided, the workload of manual identification will be reduced by 90%; if an efficient automatic identification method for asphalt pavement cracks can be provided, a sufficient basis can be provided for the traffic management departments to objectively and timely evaluate the road quality and scientifically determine the maintenance schemes.
Most of the existing identification methods for cracks utilize an “identification-first-then-classification” processing mode. Under this processing mode, the current mainstream crack identification methods based on images are mainly as follows:
(1) Crack identification method based on grayscale threshold, which selects appropriate grayscale thresholds to distinguish the image background and the targets by analyzing the grayscale features of pavement images. This method is generally based on the precondition that the grayscale of the cracks is usually lower than that of the background, and requires the cracks to be with high contrast and good continuity. However, due to the reasons of pavement fouling, crack wall exfoliation, and plentiful pavement grain texture, etc., the cracks often have features of low contrast and poor continuity, etc.; therefore the crack identification method based on grayscale threshold is difficult to identify damages with indistinctive grayscale features.
(2) Crack identification method based on morphological processing, which uses corrosion, expansion, skeleton extraction, edge detection and other methods to obtain the two-dimensional morphological features of the cracks. However, due to the complex pavement images and various damage forms, the identification method based on morphological processing is not highly practicable.
(3) Crack identification method based on machine learning, which is mainly used for the type classification after crack detection, the key thereof lies in the features extraction of the pavement cracks and the design of classifier. Due to the complex road conditions and various crack forms, the features extraction of the cracks is more difficult. Meanwhile, the accuracy, robustness and real-time of the classification algorithm are restricted by the factors of small test sample sets, complex algorithm and large calculation load etc.
(4) Identification method for pavement cracks based on multi-scale geometric analysis, which usually uses image geometric features and utilizes wavelet, ridgelet, curvelet, contourlet, bandelet and other transformations to express image information. Since the asphalt pavement cracks under complex background are irregular and the forms and locations of the cracks are unpredictable, this method cannot effectively extract complex crack information. Meanwhile, the problems of complicated calculation process and low efficiency are prevalent among multi-scale analysis methods.
Most of the existing crack detection techniques are based on good image quality; they lack the adaptability to complex environments, which is difficult to meet the practical requirements for engineering applications. Due to the influences from the factors of complex pavement structure types, uneven illumination, shadow, foreign objects on pavement and artificial marks etc., the pavement images have uneven grayscale distributions, plentiful textures, small spectrum differences, blurred edges, noise pollution and other features. The actual engineering tests show that due to the influence of natural light, street trees, buildings, pavement materials and other external factors, the collected images may have uneven illumination, large shadow, lot of debris, excessive exposure, plentiful markings and so on. Secondly, in the process of high-speed driving collection, the camera of the data collection unit and the laser cannot be absolutely maintained in the same plane due to the relative movement, so that the collected data have light and dark stripes which are shown as uneven illumination. Furthermore, the paving materials of the asphalt pavement have a strong sense of grains and are different in sizes, resulting in plentiful textures in the pavement images, which weakens or destroys the visualization features of the cracks. According to the analysis of the existing research results, the main factors that affect the crack identification are uneven illumination, shadows, marking lines, textures, etc. Uneven illumination and shadows may cover part of the features of pavement damages, which is not conducive to the extraction of pavement damage features. Interferences such as marking lines, textures etc. have some similar features with the cracks and other damaged targets, which are easy to be confused with the cracks, leading to erroneous detections. It is rather difficult to realize the crack identification by extracting crack features with high distinction directly from the original image information. Additionally, in general, the number of the images with damages in the collected highway images is in small proportion of the total number of images. If the same identification method as that for the pavement images with damages is used, the time complexity of the processing is greatly increased.
Meanwhile, most of the existing techniques are based on the assumption that the grayscale value of the cracks is lower than that of the image background, the crack target is clear, continuous and with distinct geometric features. However, on the actual pavements, due to the rolling form loaded wheels, weathering, pavement fouling, crack wall exfoliation and other reasons, the cracks usually have low contrast, poor continuity and other features. Meanwhile, since the grouting phenomenon occurs due to water flowing into the road base, the grayscale value of cracks is higher than that of the pavement background, which is shown as “white cracks” phenomenon. Thus it can be known that the assumption of the existing techniques is not fully established in practical applications. Therefore, the prior art cannot solve the problems of detecting the cracks that are small and have weak contrast, weak continuity, and detecting the “white cracks”.